Machine learning and its specific branch, deep learning, are an approaches to automatically learn the connection between input data and target data from the examples of past experiences. Machine learning is an effort to employ algorithms to devise a machine to learn and operate without explicitly planning and dictating individual actions. To be more specific, machine learning equips an inspiring platform to deduce the fundamental principles based on previously given data thus, for another given input, machines can make logical decisions automatically. With the ever-increasing evolution of machine learning and its potential capacity to handle crucial challenges, such as signal processing [1]}, and physical science [2]}, we are witnessing their applications to electromagnetic problems. Due to its remarkable potentials such as providing less computational resources, more accuracy, less design time, and more flexibility, machine learning has been entered in various wave-interaction phenomena, such as Electromagnetic Compatibility (EMC)[3]}, [4]}, Antenna Optimization and Design [5]}, [6]}, All-Dielectric Metasurface[7]}, Optical and photonic structures[8]}, and Plasmonic nanostructure[9]}.
